# Efficient Federated Conformal Prediction with Group-Conditional Guarantee

arXiv: https://arxiv.org/abs/2603.14198 · HTML: https://mlsanity.com/paper/2603.14198/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 37 references that are in the map.
- First posted on arXiv 15 Mar 2026.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Classification with Valid and Adaptive Coverage](https://mlsanity.com/paper/2006.02544/) (2020)
- [Least Ambiguous Set-Valued Classifiers with Bounded Error Levels](https://mlsanity.com/paper/1609.00451/) (2016)
- [Predictive inference with the jackknife+](https://mlsanity.com/paper/1905.02928/) (2019)
- [Conformal Prediction With Conditional Guarantees](https://mlsanity.com/paper/2305.12616/) (2023)
- [Localized Conformal Prediction: A Generalized Inference Framework for Conformal Prediction](https://mlsanity.com/paper/2106.08460/) (2021)
- [Online conformal prediction with decaying step sizes](https://mlsanity.com/paper/2402.01139/) (2024)
- [Federated Conformal Predictors for Distributed Uncertainty Quantification](https://mlsanity.com/paper/2305.17564/) (2023)
- [Conformal Prediction for Federated Uncertainty Quantification Under Label Shift](https://mlsanity.com/paper/2306.05131/) (2023)
- [One-Shot Federated Conformal Prediction](https://mlsanity.com/paper/2302.06322/) (2023)
- [Boosted Conformal Prediction Intervals](https://mlsanity.com/paper/2406.07449/) (2024)
- [Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage](https://mlsanity.com/paper/2502.17264/) (2025)
- [Conformal online model aggregation](https://mlsanity.com/paper/2403.15527/) (2024)
- [Computing Extremely Accurate Quantiles Using t-Digests](https://mlsanity.com/paper/1902.04023/) (2019)
- [Bounded Space Differentially Private Quantiles](https://mlsanity.com/paper/2201.03380/) (2022)
- [Certifiably Byzantine-Robust Federated Conformal Prediction](https://mlsanity.com/paper/2406.01960/) (2024)
- [Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints](https://mlsanity.com/paper/2409.07902/) (2024)
- [Distributed Conformal Prediction via Message Passing](https://mlsanity.com/paper/2501.14544/) (2025)
- [Marginal and training-conditional guarantees in one-shot federated conformal prediction](https://mlsanity.com/paper/2405.12567/) (2024)
- [FedCF: Fair Federated Conformal Prediction](https://mlsanity.com/paper/2509.22907/) (2025)
- [Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective](https://mlsanity.com/paper/2512.05267/) (2025)

## Built on by (most cited first, 4 of 4)

- [Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage](https://mlsanity.com/paper/2605.09986/) (2026)
- [Certified Adaptive Refresh: Anytime-Valid Monitoring for Federated Conformal RAG](https://mlsanity.com/paper/2605.29139/) (2026)
- [Multi-Agent Conformal Prediction with Personalized Statistical Validity](https://mlsanity.com/paper/2606.00717/) (2026)
- [When Average Calibration Fails: Site-Conditional Federated Conformal Risk Control](https://mlsanity.com/paper/2606.20115/) (2026)

## Abstract (arXiv)

Deploying trustworthy AI systems requires principled uncertainty quantification. Conformal prediction (CP) is a widely used framework for constructing prediction sets with distribution-free coverage guarantees. In many practical settings, including healthcare, finance, and mobile sensing, the calibration data required for CP are distributed across multiple clients, each with its own local data distribution. In this federated setting, data can often be partitioned into, potentially overlapping, groups, which may reflect client-specific strata or cross-cutting attributes such as demographic or semantic categories. We propose group-conditional federated conformal prediction (GC-FCP), a federated extension of conditional conformal calibration for a target mixture over prespecified groups. GC-FCP constructs mergeable, atom-stratified coresets from local calibration scores, enabling compact aggregation at the server when the number of active atoms is moderate. Experiments on synthetic and real-world datasets validate the performance of GC-FCP compared to centralized calibration baselines. The code of our work can be found at https://github.com/HaifengWen/GC-FCP.